Incomplete label error correction and group joint learning for gland segmentation

Abstract

Medical image segmentation methods based on deep learning show good performance advantages and application prospects, but such methods require experienced medical experts to spend a lot of time labeling data. To address this problem in gland segmentation research, a model named ILECGJL based on incomplete label error correction and group joint learning is proposed, which significantly reduces the dependence on labeled data while ensuring segmentation performance. ILECGJL employs the proposed incomplete label error correction method to capture semantic relationships between labeled and unlabeled images, enhancing the quality of pseudo labels and improving the accuracy of gland segmentation. At the same time, ILECGJL harnesses the proposed group joint learning strategy to enhance the model’s robustness on pseudo label noise, further boosting segmentation performance. Experimental results on the CRAG dataset confirm that our proposed label error correction method yields substantial improvements based on initial segmentation predictions. The performance of the ILECGJL model demonstrates significant enhancements compared to fully supervised methods that solely rely on labeled data and outperforms other mainstream semi-supervised approaches. Results and comparisons on the GlaS dataset showcase good generalization ability of the ILECGJL model. Moreover, due to its modular design and customizability, ILECGJL offers high flexibility and can be conveniently integrated into different segmentation models.